Information processing apparatus, vehicle, and information processing method using correlation between attributes

ABSTRACT

According to an embodiment, an information processing apparatus includes a memory having computer executable components stored therein; and a processing circuit communicatively coupled to the memory. The processing circuit acquires a plurality of pieces of observation information of surroundings of a moving body, generates a plurality of pieces of attribute information of the surroundings of the moving body on the basis of the plurality of pieces of observation information, and sets a reliability of the attribute information of the surroundings of the moving body on the basis of correlation of the plurality of pieces of attribute information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part application of applicationSer. No. 15/450,377 filed on Mar. 6, 2017, which claims the benefit ofpriority from Japanese Patent Application No. 2016-107751 filed on May30, 2016. This application also claims the benefit of priority fromJapanese Patent Application No. 2017-056409, filed on Mar. 22, 2017,which claims the internal priority from Japanese Patent Application No.2016-107751; the entire contents of which are incorporated herein byreference.

FIELD

Embodiments described herein relate generally to an informationprocessing apparatus, a vehicle, and an information processing method.

BACKGROUND

In relation to semantic segmentation technology to identify an attributefor each of a plurality of pixels included in an image obtained throughimaging by a camera, a method to calculate a probability (reliability)of an identification result of the attribute is known.

For example, in a convolution neural network, a technology to generate alarge number of attribute identification results from one input image bysampling and removing a unit at the time of a test, obtain an averagevalue of the attribute identification results as an attribute, andoutput a dispersion value as the reliability of the attribute is known.

However, the above-described conventional technology has problems thatan amount of processing is large because the attribute identification byremoval (dropout) of a network unit is conducted a plurality of timesfor one input image, and obtainment of sufficient accuracy is difficultbecause the reliability is calculated using only one image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram for describing an information processing apparatusof a first embodiment;

FIG. 2 is a diagram illustrating a configuration of the informationprocessing apparatus of a first embodiment;

FIG. 3 is a diagram illustrating an example of a captured image of thefirst embodiment;

FIG. 4 is a diagram illustrating an example of attribute information ofthe first embodiment;

FIG. 5 is a diagram for describing an example of a method of generatingthe attribute information of the first embodiment;

FIG. 6 is a diagram illustrating an example of the attribute informationof the first embodiment;

FIG. 7 is a diagram for describing a method of searching correspondingareas of the first embodiment;

FIG. 8 is a diagram illustrating an example of corresponding areas ofthe first embodiment;

FIGS. 9A to 9D are diagrams for describing relationship betweenattributes and reliability of the areas of the first embodiment;

FIG. 10 is a diagram illustrating an operation example of theinformation processing apparatus of the first embodiment;

FIGS. 11A and 11B are diagrams illustrating an example of attributeinformation of a second embodiment;

FIG. 12 is a diagram illustrating a configuration of an informationprocessing apparatus of a third embodiment;

FIG. 13 is a diagram illustrating an example of probability informationof the third embodiment;

FIG. 14 is a diagram illustrating an operation example of theinformation processing apparatus of the third embodiment;

FIG. 15 is a diagram illustrating a configuration of an informationprocessing apparatus of a fourth embodiment;

FIG. 16 is a diagram illustrating a configuration of an informationprocessing apparatus of a fifth embodiment; and

FIG. 17 is a diagram illustrating a configuration of an informationprocessing apparatus of a modification.

DETAILED DESCRIPTION

According to an embodiment, an information processing apparatus includesa memory having computer executable components stored therein; and aprocessing circuit communicatively coupled to the memory. The processingcircuit acquires a plurality of pieces of observation information ofsurroundings of a moving body, generates a plurality of pieces ofattribute information of the surroundings of the moving body on thebasis of the plurality of pieces of observation information, and sets areliability of the attribute information of the surroundings of themoving body on the basis of correlation of the plurality of pieces ofattribute information.

Hereinafter, an information processing apparatus, a vehicle, and aninformation processing method according to embodiments will be describedwith reference to the appended drawings.

First Embodiment

As illustrated in FIG. 1, an information processing apparatus 10 of thepresent embodiment is mounted on a vehicle. The information processingapparatus 10 is a dedicated or general-purpose computer, for example.Note that the information processing apparatus 10 may have aconfiguration in which processing by the information processingapparatus 10 is executed on a cloud, for example, without being mountedon the vehicle. Further, the type of a moving body on which theinformation processing apparatus is mounted is not limited to thevehicle and is arbitrary, and may be, for example, a robot or a drone.Note that the vehicle on which the information processing apparatus 10is mounted may be a normal vehicle that travels through a driveoperation by a person, or may be an automatic driving vehicle that canautomatically travel (autonomously travel) without through the driveoperation by a person. In the present embodiment, a case in which thevehicle on which the information processing apparatus 10 is mounted isthe automatic driving vehicle will be exemplarily described.

FIG. 2 is a diagram illustrating an example of a configuration of theinformation processing apparatus 10 of the present embodiment. Asillustrated in FIG. 2, the information processing apparatus 10 includesa processing circuit 100, a storage circuit 20, a communication unit 30,and a bus 40 that connects the circuits and units.

The processing circuit 100 includes an acquisition function 100 a, anattribute determination function 100 b, and a setting function 100 c.Specific content of these processing functions will be described below.Note that, in the example of FIG. 2, the functions in relation to thepresent embodiment are mainly exemplarily illustrated. However,functions included in the processing circuit 100 are not limited to theillustrated functions.

The processing functions performed in the information processingapparatus 10 are stored in the storage circuit 20 in a form of programsexecutable by the computer. The processing circuit 100 is a processorthat realizes the functions corresponding to the programs by reading theprograms from the storage circuit 20 and executing the programs. Theprocessing circuit 100 that has read the programs has the functionsillustrated in the processing circuit 100 of FIG. 2.

Note that, in FIG. 2, the description that the single processing circuit100 realizes the processing functions performed in the acquisitionfunction 100 a, the attribute determination function 100 b, and thesetting function 100 c has been given. However, the processing circuit100 may be configured from a combination of a plurality of independentprocessors, and the processors may execute the programs to realize thefunctions. The processing functions may be configured from the programs,and one processing circuit may execute the programs, or a specificfunction may be mounted on a dedicated independent program executingcircuit.

Note that the acquisition function 100 a included in the processingcircuit 100 may be referred to as an acquirer, the attributedetermination function 100 b may be referred to as an attributedeterminer, and the setting function 100 c may be referred to as asetter.

The wording “processor” used in the above description means, forexample, a circuit of a central processing unit (CPU), a graphicalprocessing unit (GPU), an application specific integrated circuit(ASIC), or a programmable logic device (for example, a simpleprogrammable logic device (SPLD), a complex programmable logic device(CPLD), or a field programmable gate array (FPGA)). The processorrealizes the functions by reading and executing the programs stored inthe storage circuit 20. Note that the programs may be directlyincorporated in the circuit of the processor, in place of being storedin the storage circuit 20. In this case, the processor realizes thefunctions by reading and executing the programs incorporated in thecircuit.

The storage circuit 20 stores data and the like associated with theprocessing functions performed by the processing circuit 100, as needed.The storage circuit 20 of the present embodiment stores the programs anddata used for various types of processing. For example, the storagecircuit 20 is a random access memory (RAM), a semiconductor memoryelement such as flash memory, a hard disk, an optical disk, or the like.Further, the storage circuit 20 may be substituted with a storage deviceoutside the information processing apparatus 10. The storage circuit 20may be a storage medium to which the programs transmitted through alocal area network (LAN) or the Internet are downloaded, and stored ortemporarily stored. Further, the storage medium is not limited to one,and a case in which the processing in the above embodiment is executedfrom a plurality of media is also included in the storage medium in theembodiment, and the medium can have any configuration.

The communication unit 30 is an interface that performs an input/outputof information to/from an external device connected by wired or wirelessmeans. The communication unit 30 may be connected with and performscommunication with the network.

An input device 50 receives various instructions and an input ofinformation from an operator (a driver in this example). The inputdevice 50 is, for example, a pointing device such as a mouse or atrackball, or an input device such as a keyboard.

A display 60 displays various types of information regarding thevehicle. The display 60 is, for example, a display device such as aliquid crystal display.

A sensor 70 is an external recognition sensor for realizing automaticdriving. Examples of the sensor 70 include, but are not limited to, asonar that searches for an object with a sound wave, a stereo camera foracquiring information in a depth direction of a vicinity of the vehicle,a position identifying camera for accurately identifying a place wherethe vehicle is traveling from surroundings, a millimeter wave radar or alaser sensor for measuring a distance to an object existing in thevicinity of the vehicle, and a position sensor that acquires a positionof the vehicle. In the present embodiment, at least an imaging unit(camera) for imaging the surroundings of the own vehicle is mounted asthe sensor 70.

A vehicle controller 80 determines a state of the vicinity on the basisof information obtained from the sensor 70 and an obstacle map obtainedin processing described below, and controls an acceleration amount, abrake amount, a steering angle, and the like, in order to automaticallydrive the vehicle. To be specific, the vehicle controller 80 controlsthe vehicle to keep a lane on which the vehicle is currently travelingwhile avoiding an obstacle, and keep a distance from a vehicle in frontby a predetermined distance or more.

The input device 50, the display 60, the sensor 70, and the vehiclecontroller 80 in the present embodiment are connected with theinformation processing apparatus 10 by wired or wireless means.

Next, the functions included in the processing circuit 100 will bedescribed. The acquisition function 100 a acquires a plurality of piecesof observation information of surroundings of a moving body (a vehiclein this example). Further, the acquisition function 100 a acquiresposition information indicating an observation position. To be specific,the acquisition function 100 a acquires a plurality of first sets, eachof the first sets indicating a combination of the above-describedobservation information indicating a result of observation of thesurroundings of the own vehicle, and the position information indicatingthe observation position. The observation information of the presentembodiment is a captured image obtained through imaging by the imagingunit that images the surroundings of the vehicle. In this example, theimaging unit is attached to the vehicle, and thus the positioninformation indicating the observation position (imaging position)corresponds to information (own position and posture information)indicating a position and a posture of the own vehicle. Note that theimaging refers to conversion of an image of a subject formed by anoptical system such as a lens into an electrical signal.

The acquisition function 100 a of the present embodiment acquires theset (first set) of the captured image obtained through the imaging andthe position information (in this example, the own position and postureinformation of the vehicle) indicating the imaging position every timethe imaging by the imaging unit is performed. That is, the acquisitionfunction 100 a acquires a plurality of the first sets in time series.However, an embodiment is not limited thereto, and for example, aplurality of the imaging units may be provided in different positions(for example, provided outside the vehicle), and a plurality of thefirst sets (sets of the captured image and the position informationindicating the imaging position) corresponding on a one-to-one basis tothe plurality of imaging units may be acquired at the same timing. Inshort, the acquisition function 100 a may just acquire a plurality ofthe first sets by changing the condition of the observation, each of thefirst sets indicating the combination of the observation information(the captured image in this example) indicating a result of theobservation of the surroundings of the own vehicle, and the positioninformation indicating the observation position. The acquisitionfunction 100 a passes the captured images and the position informationincluded in the acquired first sets to the attribute determinationfunction 100 b.

FIG. 3 is a diagram illustrating an example of the captured image. Thecaptured image illustrated in FIG. 3 is a captured image obtained byimaging the front of the own vehicle. A roadway, sidewalks beside theroadway, parked vehicles (other vehicles), buildings, and the like arecaptured on the image. The acquisition function 100 a of the presentembodiment acquires the captured images of a range in relation totraveling of the own vehicle, like FIG. 3, in time series (theacquisition function 100 a acquires a plurality of the captured imagesin time series) obtained through imaging by the imaging unit attached tothe own vehicle.

As described above, in the present embodiment, the position informationindicating the imaging position is the information (own position andposture information) indicating the position and the posture of thevehicle (own vehicle) to which the imaging unit is attached. Coordinateinformation indicating the position of the own vehicle can be acquiredusing a GPS mounted on the vehicle, and the posture information of theown vehicle can be similarly acquired using an inertial measurement unit(IMU) mounted on the vehicle. Note that the coordinate informationindicating the position of the own vehicle is world coordinates based ona certain position.

Description of the functions included in the processing circuit 100 iscontinued. The attribute determination function 100 b generates, foreach of a plurality of areas obtained by dividing the surroundings ofthe vehicle, attribute information (attribute map) indicating anattribute of the area, on the basis of the observation information (thecaptured image in this example). To be specific, the attributedetermination function 100 b determines an attribute for each of aplurality of areas obtained by dividing the surroundings of the vehicleon the basis of the observation information (the captured image in thisexample) to generate the above-described attribute information, andgenerates second sets, each of the second sets indicating a combinationof the generated attribute information and the position information. The“attribute” refer to information classified into a plurality ofcategories, and examples of the categories include, but are not limitedto, a roadway, a sidewalk, a white line, a vehicle, a building, apedestrian, and a traffic light. As the categories, statuses indicatingavailability of traveling such as travelable, untravelable, and unknownmay be employed.

In this example, the attribute determination function 100 b determines(identifies) the attribute in a pixel level, for an object captured inthe image. This attribute determination (attribute identification) canbe realized using machine learning. Many methods of the attributeidentification of an image using machine learning are known. Forexample, the attribute can be determined (identified) in a pixel level,using a method such as J. Long, et. al, “Fully Convolutional Networksfor Semantic Segmentation”, CVPR 2015, or V. Badrinarayanan, et. al,“SegNet: A Deep Convolutional Encoder-Decoder Architecture for RobustSemantic Pixel-Wise Labelling”, CVPR 2015. Please see the documents fordetails of the methods.

The attribute determination function 100 b determines the attribute ofeach of pixels included in the captured image of FIG. 3, using theabove-exemplarily described known method. Then, the attributedetermination function 100 b sets pixel values of the pixels to valuesindicating the determined attributes to generate the attributeinformation illustrated in FIG. 4.

Here, the attribute determination function 100 b holds positionalrelationship information indicating positional relationship between theimaging unit and a road surface. The attribute determination function100 b projects the attribute information on the road surface on thebasis of the positional relationship information, and then transforms animage projected on the road surface into an image (upper surface image)as viewed from above the road surface. The transformation of the imageprojected on the road surface into the upper surface image can betypically realized using a widely known method called inverseperspective mapping. An outline of the inverse perspective mapping willbe described using FIG. 5. Here, a traveling direction is a z axis, aheight direction is a y axis, and a direction perpendicular to the zaxis and the y axis is an x axis. Coordinates of a square area dividedfrom the road surface when the road surface is viewed from above are(xi, zi). Since the position and posture of the imaging unit withrespect to the road surface are known, a pixel p1 corresponding to thecoordinates (xi, zi) of the area, of the captured image, can be obtainedby perspective projection transformation using the positionalrelationship information of the imaging unit and the road surface.Similarly, a pixel p2 corresponding to the coordinates (xi, zi) of thearea, of the upper surface image, can be obtained by perspectiveprojection transformation using the positional relationship informationof a position specified as a viewpoint of the upper surface image (topview) and the road surface. A pixel value of the pixel p1 of thecaptured image is allocated as a pixel value of the pixel p2 of theupper surface image, whereby the pixel values of the pixels of the uppersurface image can be set.

FIG. 6 is a diagram illustrating the upper surface image transformedfrom the attribute information of FIG. 4. The attribute informationgenerated from the captured image is transformed into the image (uppersurface image) corresponding to a viewpoint of when the road surface islooked down from above. Here, the upper surface image (the size, thepositions, the numbers, and the attributes of the square areas areidentifiable) is sent to the setting function 100 c as the attributeinformation.

The setting function 100 c sets a reliability of the attributeinformation of the surroundings of the moving body on the basis ofcorrelation of a plurality of pieces of the attribute information.Further, the setting function 100 c sets a reliability in areas, from aplurality of pieces of the attribute information for the areascorresponding to the same position, on the basis of the positioninformation. In the present embodiment, the setting function 100 c setsa reliability of an attribute of an area of target attributeinformation, from correlation between the attribute of the areaindicated by the target attribute information, and attributes of one ormore corresponding areas indicating areas corresponding to the area ofthe target attribute information, in the other one or more pieces of theattribute information, using a plurality of pieces of the attributeinformation. In the present embodiment, the setting function 100 c sets,on the basis of a plurality of the second sets, a reliability of anattribute of an area of a target second set, from correlation betweenthe attribute of the area indicated by the attribute informationincluded in the target second set, and attributes of corresponding areasin the other one or more pieces of the second sets. To be specific, thesetting function 100 c sets, using a plurality of the second sets, foreach of a plurality of areas corresponding to a target second set, thereliability of the attribute of the area of the target second set, fromcorrelation between the attribute of the area of the target second set,and the attribute of one or more corresponding areas indicating areas ofa plurality of areas of each of the other one or more second sets, theareas corresponding to the area of the target second set. In the presentembodiment, the setting function 100 c sets, every time receiving thesecond set from the attribute determination function 100 b, using thereceived second set (target second set) and N second sets received inthe past, for each of a plurality of areas corresponding to theattribute information included in the target second set (the pluralityof areas can be considered to be a plurality of areas obtained bydividing the surroundings of the own vehicle at the point of time whenthe target second set is acquired), the reliability of the attribute ofthe area of the target second set, from correlation between theattribute of the area of the target second set, and the attribute of oneor more corresponding areas of a plurality of areas of each of N piecesof attribute information corresponding on a one-to-one basis to the Nsecond sets received in the past, the one or more corresponding areascorresponding to the area of the target second set. Hereinafter,specific content will be described.

The setting function 100 c first, for each of areas of the attributeinformation included in the latest second set, searches for an area(corresponding area) corresponding to the area of the latest second setand identifies the attribute of the area (corresponding area), for eachof N pieces of attribute information corresponding on a one-to-one basisto the past N second sets, using the position information (own positionand posture information) corresponding to the attribute information, andthe position information included in the latest second set. That is, thesetting function 100 c identifies the area corresponding to the sameposition of the area indicated by the target second set, as thecorresponding area, of a plurality of areas of each of one or moresecond sets (the other one or more second sets) other than the targetsecond set, on the basis of the position information. Details will bedescribed using FIG. 7. FIG. 7 illustrates a plurality of areas obtainedby dividing a space of the surroundings around the own vehicle at timest−1 and t. An area N_(t−1) at the time t−1 and an area N_(t) at the timet have different relative positions from the own vehicle at therespective times, but indicate the same position in the world coordinatesystem. The amount of movement of the own vehicle between the time t andits previous time t−1 is calculated from the position information, andareas at the time t−1 corresponding to the areas at the time t areobtained on the basis of the amount of movement of the own vehicle. Inthe example of FIG. 7, an area N_(t−1) at the time t−1 corresponding toan area N_(t) at the time t is obtained. For convenience of description,in the example of FIG. 7, the corresponding area in the attributeinformation included in one second set immediately before the latestsecond set has been acquired. However, an embodiment is not limitedthereto, and corresponding areas can be obtained throughout a pluralityof second sets (attribute information) acquired in the past, and itsattributes can be obtained.

Next, a method of setting the reliability by the setting function 100 cwill be described. The setting function 100 c of the present embodimentsets the reliability to a higher value as the correlation of a pluralityof pieces of the attribute information is higher, and sets thereliability to a lower value as the correlation of the plurality ofpieces of attribute information is lower. To be specific, the settingfunction 100 c sets the reliability corresponding to the area to ahigher value as the correlation between the attribute of the areaindicated by the target second set and the attribute of the one or morecorresponding areas is higher, and sets the reliability corresponding tothe area to a lower value as the correlation between the attribute ofthe area indicated by the target second set and the attribute of the oneor more corresponding areas is lower. To be specific, the settingfunction 100 c sets the reliability corresponding to the area to ahigher value as the number of the corresponding areas indicating thesame attribute as the attribute of the area indicated by the targetsecond set is larger, and sets the reliability corresponding to the areato a lower value as the number of the corresponding areas indicating thesame attribute as the attribute of the area indicated by the targetsecond set is smaller. Here, the setting function 100 c sets, for eachof the plurality of areas corresponding to the target second set, thereliability corresponding to the area to a higher value as the number ofthe corresponding areas indicating the same attribute as the attributeof the area is larger, and sets the reliability corresponding to thearea to a lower value as the number of the corresponding areasindicating the same attribute as the attribute of the area is smaller.

The method of calculating the reliability will be described in detailusing FIG. 8. FIG. 8 is a diagram illustrating attributes of a pluralityof areas obtained by dividing the surroundings of the own vehicle at theconsecutive times t−2, t−1, and t. In FIG. 8, the areas displayed in“white” indicate areas where the attribute is identified as the roadway,and the areas displayed in a “shaded” manner indicate areas where theattribute is identified as other than the roadway. In the example ofFIG. 8, the attribute of an area 1 is identified as the roadway at anyof the times t−2, t−1, and t. Meanwhile, the attribute of an area 2 isidentified as other than the roadway at the times t−2 and t−1, and isidentified as the roadway at the time t. With regard to the attributeinformation for the captured image, while the areas existing inside theareas indicating the same attribute have a low difficulty in theattribute identification, and tend to take a correct attribute in timeseries, the areas existing near a boundary of the areas indicatingdifferent attributes have a high difficulty in the attributeidentification and wrong identification is more likely to occur, andthus the attribute tends to be changed in time series. Therefore, withrespect to the attributes of the areas of the current time (the time atwhich the latest second set is acquired), the reliability of theattributes of the areas at the current time is set higher as the numberof times at which the attributes of areas corresponding to the samepositions in the world coordinate system as the areas at the currenttime are the same is larger at times corresponding to the past N secondsets (attribute information) determined in advance. Meanwhile, thereliability of the attributes of the areas at the current time is setlower as the number of times at which the attributes of areascorresponding to the same positions in the world coordinate system asthe areas at the current time are the same is smaller at the timescorresponding to the past N second sets determined in advance. Forexample, reliability C_(L1) of an attribute L1 of a case where theattribute of a certain area at the current time is L1 can be calculatedby Equation (1):

$\begin{matrix}{C_{L\; 1} = \frac{N_{L\; 1}}{N}} & (1)\end{matrix}$where the number of times at which the attribute of the correspondingareas is L1 at times corresponding to the past N second sets is N_(L1).

As illustrated in FIG. 8, in a case where the areas have two types ofattributes, an attribute value of 0 or 1 is provided, and average valuesof the past attribute values are held in the areas, so that thereliability can be calculated without holding all of the past Nattribute information. In this case, reliability C(t) at the time t canbe calculated by Equations (2):

$\begin{matrix}{{{L^{\prime}(t)} = \frac{{{L^{\prime}\left( {t - 1} \right)} \times \left( {N - 1} \right)} + {L(t)}}{N}}{{C(t)} = {1 - {{{L(t)} - {L^{\prime}(t)}}}}}} & (2)\end{matrix}$where the attribute value at the time t is L(t), and the average valueof the attribute values of the past N frames up to the time t−1 isL′(t−1).

Next, relationship between the attribute and the reliability in theareas will be described using FIGS. 9A to 9D. In the scene illustratedin FIG. 9A, the attributes and the reliability of the areas of thesurroundings of the own vehicle are obtained. The scene illustrated inFIG. 9A is a scene in which the roadway on which the own vehicle travelsand the sidewalks on both sides of the roadway exist. FIG. 9B is adiagram illustrating a result teaching correct attributes for the sceneillustrated in FIG. 9A. In the areas, the area corresponding to theroadway is illustrated by display corresponding to the attributeindicating the roadway (the area is displayed in “white” in FIGS. 9A and9B), and the area corresponding to the sidewalk is illustrated bydisplay corresponding to the attribute indicating the sidewalk (the areais displayed in a “shaded” manner in FIGS. 9A and 9B). In contrast, FIG.9C is a diagram illustrating a result of identification of theattributes of the areas, using machine learning, by the attributedetermination function 100 b. Compared with FIG. 9B, wrongidentification occurs in the areas positioned near the boundary of theroadway and the sidewalks. This tendency similarly occurs in anidentification result of the attributes for the captured image obtainedthrough imaging at different timing. FIG. 9D is a diagram illustratingthe reliability set to each of the areas on the basis of theidentification results. As described above, the setting function 100 cof the present embodiment sets the reliability of the areas on the basisof time-series correlation of the attributes of the areas. Therefore,while the reliability is set to a high value in the vicinity of thecenter of the roadway and the sidewalks distant from the roadway wherethe identification results are stable in time series (these areas aredisplayed in “white” in FIG. 9D), the reliability is set to a low valuein the areas near the boundary of the roadway and the sidewalks wherethe identification results are unstable in time series (these areas aredisplayed in “dark gray” in FIG. 9D). In the example of FIGS. 9A to 9D,a case in which two types of the attributes including the roadway andthe sidewalk has been described. However, in a case of three or moreattributes, the reliability can be similarly set on the basis of thetime-series correlation of the attributes of the areas. As describedabove, the setting function 100 c can set the reliability to each of theplurality of areas corresponding to the attribute information includedin the target second set.

Note that, in the present embodiment, the reliability of the areas hasbeen set on the basis of the time-series correlation of the attributesof the areas. However, for example, the above-described method (themethod of setting the reliability) can be applied to a form in which theacquisition function 100 a acquires a plurality of the first sets (setsof the captured image and the position information) at the same timing.In this case, the setting function 100 c acquires a plurality of thesecond sets at the same timing. Therefore, any of the plurality ofacquired second sets is employed as the target second set, and thereliability can be set to each of a plurality of the areas correspondingto the attribute information included in the target second set by theabove-described method. As described above, calculation accuracy of thereliability of the areas can be improved by setting the reliability ofeach of the plurality of areas corresponding to the target second set,using the plurality of second sets having different observationconditions.

FIG. 10 is a flowchart illustrating an operation example of theinformation processing apparatus 10 (processing circuit 100) describedabove. Specific content of the steps has been described above, and thusdetailed description is appropriately omitted. First, the acquisitionfunction 100 a acquires the captured image and the own position andposture information (the first set) (step S1). Next, the attributedetermination function 100 b determines the attribute for each of theplurality of areas obtained by dividing the surroundings of the ownvehicle, on the basis of the captured image acquired in step S1, andgenerates the attribute information indicating the attributes of theplurality of areas (step S2). Next, the setting function 100 c sets, foreach of the plurality of areas corresponding to the attributeinformation generated in step S2, the reliability on the basis of thetime-series correlation of the attribute of the area (step S3).

As described above, in the present embodiment, the reliability of theattribute of the area indicated by the target second set is set, fromthe correlation between the attribute of the area indicated by thetarget second set, and the attribute of one or more corresponding areasindicating areas of each of the other one or more second sets, the areascorresponding to the area indicated by the target second set, on thebasis of the plurality of second sets. Accordingly, it is not necessaryto repeat the processing for identifying the attribute a plurality oftimes, for one input image (captured image). In addition, thecalculation accuracy of the reliability can be improved, compared withthe method of calculating the reliability using only one input image.That is, according to the present embodiment, the reliability of theattributes of the areas of the surroundings of the own vehicle can beaccurately calculated with a small amount of processing.

Second Embodiment

Next, a second embodiment will be described. Description of a portioncommon to that of the above-described first embodiment is appropriatelyomitted. In the present embodiment, observation information acquired byan acquisition function 100 a is different from that of the firstembodiment in that the observation information is information indicatinga position of an object existing in surroundings of an own vehicle.

In this example, a distance sensor is attached to the own vehicle. Thedistance sensor radiates radio waves to surroundings of the own vehicle,and measures a distance from the own vehicle to the object by comparinga reflected wave from the object and a radiation wave. Then, theacquisition function 100 a acquires a combination of positioninformation of the object existing in the surroundings of the ownvehicle, which has been measured by the distance sensor, and positioninformation (own position and posture information of the vehicle in thiscase) indicating a measurement position, as a first set.

Here, a case in which the acquisition function 100 a acquires positioninformation (three-dimensional information) of the object existing inthe surroundings of the own vehicle at different timing according totraveling of the own vehicle will be described. However, the positioninformation of the object may be information measured by the distancesensor mounted on the vehicle or may be information acquired from anoutside by communication means. Note that the position information ofthe object (dot) is acquired as information indicating a relativeposition based on a measurement position. Further, the acquisitionfunction 100 a acquires the position information of the distance sensorof when the position of the object is measured similarly to theabove-described first embodiment. As described above, in the presentembodiment, the position information of the object is measured using thedistance sensor mounted on the vehicle. Therefore, similarly to thefirst embodiment, the acquisition function 100 a acquires the ownposition and posture information, using a GPS or an IMU mounted on thevehicle. The acquisition function 100 a acquires a plurality of thefirst sets in time series, and passes the position information(information indicating the position of the object existing in thesurroundings of the own vehicle) and the own position and postureinformation (corresponding to information indicating the measuredposition) included in the acquired first set to an attributedetermination function 100 b, every time acquiring the first set.

The attribute determination function 100 b determines an attribute foreach of a plurality of areas obtained by dividing the surroundings ofthe own vehicle on the basis of the position information passed from theacquisition function 100 a, and generates a second set indicating acombination of attribute information indicating the attribute of each ofthe plurality of areas, and the own position and posture information.The attributes here are information indicating availability of travelingof the own vehicle. The attribute determination function 100 bdetermines that an obstacle exists in an area that includes a dotcorresponding to the object that exists in a position closest to the ownvehicle, and sets an attribute indicating untravelable, for each of aplurality of angle directions (angle directions from the own vehicle),for a space of the surroundings of the own vehicle. Further, in theangle directions, the attribute determination function 100 b determinesthat no obstacle exists in an area closer to the own vehicle than thearea that includes the dot existing in the position closest to the ownvehicle, and sets an attribute indicating travelable. Further, in theangle directions, the attribute determination function 100 b sets anattribute indicating unknown to an area more distant from the ownvehicle than the area that includes the dot existing in the positionclosest to the own vehicle because the area is blocked by the obstacle.

The plurality of areas obtained by dividing the surroundings of the ownvehicle and the attributes set to the areas will be described usingFIGS. 11A and 11B. FIG. 11A illustrates dots of objects (objectsexisting in the surroundings of the own vehicle) measured in a scene inwhich the own vehicle approaches a T-junction surrounded by fences. Anexample of a result (that is, the attribute information) obtained bydetermining the attribute for each of the plurality of areas obtained bydividing the surroundings of the own vehicle in the scene of FIG. 11Aand setting the determined attributes is illustrated in FIG. 11B. Asillustrated in FIG. 11B, the attribute indicating untravelable is set toareas that include the dots corresponding to the fences. The attributeindicating travelable is set to areas positioned between the own vehicleand the areas to which the attribute indicating untravelable is set. Theattribute indicating unknown is set to areas blocked by the areas towhich the attribute indicating untravelable is set. In the example ofFIGS. 11A and 11B, the attributes are information classified into thethree types of categories including travelable, untravelable, andunknown. However, the attributes may be information classified into twotypes of categories including travelable and untravelable, where unknownis regarded as untravelable.

As described above, the attribute determination function 100 b generatesthe second set every time receiving the position information, and theown position and posture information from the acquisition function 100a, and sends the generated second set to a setting function 100 c. Thefunction of the setting function 100 c is similar to that of the firstembodiment and thus detailed description is omitted.

Third Embodiment

Next, a third embodiment will be described. Description of a portioncommon to that of the above-described embodiments is appropriatelyomitted.

FIG. 12 is a diagram illustrating an example of a configuration of aninformation processing apparatus 10 of the present embodiment. Asillustrated in FIG. 12, a processing circuit 100 further includes aprobability calculation function 100 d and a determination function 100e.

Further, in the present embodiment, a plurality of sensors for observingsurroundings of an own vehicle is attached to the own vehicle. In thisexample, the imaging unit described in the first embodiment and thedistance sensor described in the second embodiment are attached to theown vehicle. However, the sensors are not limited thereto. For example,two or more imaging units having different characteristics (parametersor the like) may be attached to the own vehicle, or two or more distancesensors having different characteristics may be attached to the ownvehicle. Further, for example, two or more imaging units having the samecharacteristic or different characteristics may be provided in differentpositions outside the vehicle, or two or more distance sensors havingthe same characteristic or different characteristics may be provided indifferent positions outside the vehicle. Then, an acquisition function100 a acquires, for each of the plurality of sensors, a first setindicating a combination of observation information (for example, acaptured image and position information of an object) and an observationposition (for example, own position and posture information or thelike). In short, the acquisition function 100 a may just acquire aplurality of first sets for each of the plurality of sensors. In thisexample, the acquisition function 100 a sends the observationinformation, and the own position and posture information included inthe acquired first set to an attribute determination function 100 bevery time acquiring the first set of each of the plurality of sensors.

The attribute determination function 100 b determines, for each of theplurality of sensors, an attribute for each of a plurality of areasobtained by dividing the surroundings of the own vehicle, on the basisof the observation information included in the first set correspondingto the sensor, generates a second set indicating a combination ofattribute information indicating the attribute of each of the pluralityof areas, and the own position and posture information, and sends thegenerated second set to a setting function 100 c and a probabilitycalculation function 100 d. Note that, for example, the attributedetermination function 100 b may send only the attribute information tothe probability calculation function 100 d. Specific content of a methodof generating the attribute information is as described in the aboveembodiments.

The setting function 100 c sets, for each of the plurality of sensors,using the plurality of second sets (sets of the attribute informationand the position information) corresponding to the sensor, for each ofthe plurality of areas corresponding to a target second set, reliabilityof the attribute of the area of the target second set, from correlationbetween the attribute of the area of the target second set, and theattributes of one or more corresponding areas. Specific content is asdescribed in the above embodiments. That is, the setting function 100 cgenerates, for each of the plurality of sensors, reliability information(a plurality of pieces of reliability information corresponding on aone-to-one basis to the plurality of sensors) indicating the reliabilityof the areas, and sends the generated reliability information to thedetermination function 100 e.

The probability calculation function 100 d calculates, for each of theplurality of areas obtained by dividing the surroundings of the ownvehicle, a travelable probability indicating a probability that the ownvehicle is travelable in the area, on the basis of the attributeinformation. In this example, the probability calculation function 100 dcalculates the travelable probability of the area on the basis of theattribute of the area, for each of the plurality of areas correspondingto the received attribute information, every time receiving theattribute information from the attribute determination function 100 b.Here, while the travelable probabilities of the areas can be consideredsynonymous with probabilities (obstacle existing probabilities) that anobject exists in the areas, the travelable probability becomes lower asthe obstacle existing probability is higher. For example, relationshipbetween the travelable probability and the obstacle existing probabilitycan be expressed by Equation (3):P _(free)=1−P _(obst)  (3)where p_(free) represents the travelable probability, and p_(obst)represents the obstacle existing probability.

The probability calculation function 100 d generates, for each of theplurality of sensors, the probability information indicating thetravelable probabilities of the areas, and sends the generatedprobability information to the determination function 100 e.

For example, as described in the first embodiment, assuming a case inwhich attributes indicating a roadway, a sidewalk, a white line, avehicle, a building, a pedestrian, a traffic light, and the like are setto the areas as the attribute information. In this example, theprobability calculation function 100 d considers that an object(obstacle) does not exist only in areas where the attribute isidentified (determined) as the roadway and the own vehicle istravelable, and sets the travelable probability to a maximum value(1.0). The probability calculation function 100 d considers that the ownvehicle is untravelable in areas where the attribute is identified asother than the roadway, and sets the travelable probability to a minimumvalue (0.0). Note that, as for the transformation from the attributeinformation to the travelable probability, values of the travelableprobabilities may be set in multi-stages according to identificationresults, instead of two selections of the minimum value (0.0) and themaximum value (1.0). In short, the probability calculation function 100d can calculates the value of the travelable probability of the areawhere the attribute is the roadway to be higher than the value of thetravelable probability of the area where the attribute is other than theroadway. FIG. 13 is a diagram illustrating an example in which theattribute information of FIG. 6 is transformed into the probabilityinformation. The travelable probabilities of the areas where theattribute is identified as the roadway in FIG. 6 are set to the maximumvalue (1.0) (the areas are displayed in “white” in FIG. 13), and thetravelable probabilities of the areas where the attribute is identifiedas other than the roadway in FIG. 6 are set to the minimum value (0.0)(the areas are displayed in “black” in FIG. 13).

Further, for example, as described in the second embodiment, assuming acase in which the attributes indicating travelable, untravelable, andunknown are set to the areas as the attribute information. In thisexample, the probability calculation function 100 d sets the travelableprobabilities of areas in which the attribute is identified (determined)as travelable to the maximum value (1.0), sets the travelableprobabilities of areas where the attribute is identified (determined) asunknown to an intermediate value (0.5), and sets the travelableprobabilities of areas in which the attribute is identified (determined)as untravelable to the minimum value (0.0).

As described above, the probability calculation function 100 d generatesthe probability information on the basis of the attribute informationcorresponding to the plurality of sensors. That is, the probabilitycalculation function 100 d generates the probability information foreach of the plurality of sensors, and sends the generated probabilityinformation to the determination function 100 e.

The determination function 100 e acquires, for each of the plurality ofsensors, the probability information and the reliability informationdescribed above, and determines final probabilities of the travelableprobabilities of the plurality of areas obtained by dividing thesurroundings of the own vehicle on the basis of the probabilityinformation and the reliability information of each of the plurality ofsensors. Note that the numbers of the plurality of areas (the pluralityof areas obtained by dividing the surroundings of the own vehicle) andsizes and positions of the areas corresponding to the probabilityinformation and the reliability information of the plurality of sensorscorrespond to one another (they may not be perfectly matched).

For example, the determination function 100 e can determine, for each ofthe plurality of areas obtained by dividing the surroundings of the ownvehicle, the travelable probability corresponding to the sensor havingthe highest reliability as the final probability. For example, in a casewhere the number of sensors is two, the final probability can becalculated by Equations (4):P _(free) =p1_(free)(C1>C2)P _(free) =p2_(free)(C1≤C2)  (4)In Equations (4), p_(free) represents the final probability, p1_(free)represents the travelable probability corresponding to one of thesensors (the one is referred to as “sensor 1”), p2_(free) represents thetravelable probability corresponding to the other sensor (referred to as“sensor 2”), c1 represents the reliability corresponding to the sensor1, and c2 represents the reliability corresponding to the sensor 2.

Further, for example, the determination function 100 e can determine,for each of the plurality of areas obtained by dividing the surroundingsof the own vehicle, the final probability by performing weighted summingof the travelable probabilities of each of the plurality of sensorsaccording to the reliability of each of the plurality of sensors. Forexample, in a case where the number of sensors is two, the finalprobability can be calculated by Equation (5):P _(free) =p1_(free) ×w1+p2_(free) ×w2  (5)In Equation (5), p_(free) represents the final probability, p1_(free)represents the travelable probability corresponding to one of thesensors (the one is referred to as “sensor 1), p2_(free) represents thetravelable probability corresponding to the other sensor (referred to as“sensor 2”), w1 represents a weight of the sensor 1, and w2 representsthe weight of the sensor 2. The weight w1 of the sensor 1 is expressedby c1/(c1+c2), and the weight w2 of the sensor 2 is expressed byc2/(c1+c2). c1 represents the reliability corresponding to the sensor 1,and c2 represents the reliability corresponding to the sensor 2.

As described above, the determination function 100 e determines thefinal probability for each of the plurality of areas, thereby to obtain,for each of the plurality of areas, an obstacle map indicating the finalprobability of the area.

FIG. 13 is a flowchart illustrating an operation example of theinformation processing apparatus 10 (processing circuit 100) of thepresent embodiment. Detailed description is appropriately omitted asspecific content of steps has been described. First, the acquisitionfunction 100 a acquires the first set (the combination of theobservation information and the position information) for each of theplurality of sensors (step S11). Next, the attribute determinationfunction 100 b determines, for each of the plurality of sensors, theattribute for each of the plurality of areas obtained by dividing thesurroundings of the own vehicle on the basis of the observationinformation corresponding to the sensor, and generates the attributeinformation indicating the attributes of the plurality of areas (stepS12). Next, the setting function 100 c sets, for each of the pluralityof sensors, the reliability of the areas, using the latest second setindicating the combination of the position information included in thefirst set acquired in step S11 and the attribute information generatedin step S12, and one or more past second sets, thereby to generate, foreach of the plurality of sensors, the reliability information indicatingthe reliability of the areas (step S13). Next, the probabilitycalculation function 100 d calculates, for each of the plurality ofsensors, the travelable probabilities of the areas on the basis of theattribute information corresponding to the sensor, thereby to generate,for each of the plurality of sensors, the probability informationindicating the travelable probabilities of the areas (step S14). Next,the determination function 100 e determines, for each of the pluralityof sensors, the final probabilities of the travelable probabilities ofthe areas on the basis of the probability information and thereliability information corresponding to the sensor (step S15).

Fourth Embodiment

Next, a fourth embodiment will be described. Description of a portioncommon to that of the above-described third embodiment is appropriatelyomitted. FIG. 15 is a diagram illustrating an example of a configurationof an information processing apparatus 10 of the present embodiment. Asillustrated in FIG. 15, a processing circuit 100 of the presentembodiment is different from the above-described first embodiment infurther including an output function 100 f as an example of “outputunit”. In this example, the output function 100 f outputs (displays, ona display 60), for each of a plurality of areas, an obstacle mapindicating a final probability of the area. Note that the outputfunction 100 f may have a function to generate the obstacle map, or thefunction to generate the obstacle map may be provided separately fromthe output function 100 f, for example. For example, the above-describeddetermination function 100 e may have the function to generate theobstacle map. As described above, the information processing apparatus10 of the present embodiment includes a display unit that displaysinformation regarding an existence probability of an object existing insurroundings of a moving body, which is calculated on the basis ofattribute information of the surroundings of the moving body and areliability of the attribute information. As described above, thereliability is calculated using a plurality of pieces of the attributeinformation for the same area. Further, in this example, the existenceprobability of the object is the above-described obstacle map. In thisexample, it may be considered that the output function 100 f correspondsto the “display unit”, the display 60 corresponds to the “display unit”,or a combination of the output function 100 f and the display 60corresponds to the “display unit”.

Fifth Embodiment

Next, a fifth embodiment will be described. Description of a portioncommon to that of the above-described third embodiment is appropriatelyomitted. FIG. 16 is a diagram illustrating an example of a configurationof an information processing apparatus 10 of the present embodiment. Asillustrated in FIG. 16, a processing circuit 100 of the presentembodiment is different from the first embodiment in further including arecoding function 100 g as an example of “recording unit”. Here, therecording function 100 g records (records, in a storage circuit 20, forexample), for each of a plurality of areas obtained by dividingsurrounding of an own vehicle, an obstacle map indicating a finalprobability of the area. That is, the information processing apparatus10 of the present embodiment further includes a storage unit (a storagecircuit 20, for example) that stores the final probability (the obstaclemap, in this example). Further, as described in the third embodiment,the processing circuit 100 may further include the above-describedoutput function 100 f. Further, for example, the information processingapparatus 10 may include a storage unit (the storage circuit 20, or thelike, for example) that stores a travelable probability.

A vehicle controller 80 estimates a travel route avoiding an obstacle onthe basis of the obstacle map recorded by the recording function 100 g.As an estimation method, various known technologies can be used. Thevehicle controller 80 then controls a vehicle to travel (autonomouslytravel) according to the estimated travel route.

For example, as illustrated in FIG. 17, the information processingapparatus 10 may further include a control function 100 h. The controlfunction 100 h is an example of “controller”, and calculates controlinformation of the vehicle on the basis of the final probability. Inthis example, the control function 100 h calculates (estimates) thetravel route (an example of the control information) on the basis of theobstacle map. Note that, for example, the control function 100 h mayalso serve the function of the vehicle controller 80, without providingthe vehicle controller 80. Further, for example, when the informationprocessing apparatus 10 includes the storage unit that stores thetravelable probability, the control function 100 h can calculate thecontrol information of the vehicle on the basis of the travelableprobability.

The embodiments of the present invention have been described. However,the above-described embodiments have been presented as examples, and arenot intended to limit the scope of the invention. These new embodimentscan be implemented in other various forms, and various omissions,replacements, and changes can be made without departing from the gist ofthe invention. These new embodiments and its modifications are includedin the scope and the gist of the invention, and are included in theinvention described in the scope of claims and its equivalents.

Further, the programs executed in the information processing apparatus10 of the embodiments and modifications may be stored on a computerconnected to a network such as the Internet, and provided by beingdownloaded through the network. Further, the programs executed in theinformation processing apparatus 10 of the embodiments and modificationsmay be provided or distributed through the network such as the Internet.The programs executed in the information processing apparatus 10 may beincorporated in a non-volatile recording medium such as a ROM in advanceand provided.

Further, the embodiments and modifications can be arbitrarily combined.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

REFERENCE SIGNS LIST

-   10 information processing apparatus-   20 storage circuit-   30 communication unit-   40 bus-   50 input device-   60 display-   70 sensor-   80 vehicle controller-   100 processing circuit-   100 a acquisition function-   100 b attribute determination function-   100 c setting function-   100 d probability calculation function-   100 e determination function-   100 f output function-   100 g recording function-   100 h control function

What is claimed is:
 1. An information processing apparatus, comprising: a memory having computer executable components stored therein; and processing circuitry communicatively coupled to the memory, the processing circuitry configured to: acquire a plurality of pieces of observation information of surroundings of a moving body; generate a plurality of pieces of attribute information of the surroundings of the moving body based on the plurality of pieces of observation information; and set a reliability of each of the plurality of pieces of attribute information of the surroundings of the moving body based on attribute information included in one or more past pieces of attribute information corresponding to an area at a same position as each respective piece of attribute information, wherein the processing circuitry is further configured to set the reliability of a piece of attribute information corresponding to a given area based on a difference between (1) a proportion of the one or more past pieces of attribute information having a same value as the piece of attribute information, and (2) a numerical value of the piece of attribute information corresponding to the given area, and at least one of the one or more past pieces of attribute information used to set the reliability of the piece of attribute information corresponding to the given area corresponds to a different position of the moving body than a position of the moving body corresponding to the piece of attribute information for the given area.
 2. The apparatus according to claim 1, wherein the processing circuitry is further configured to acquire position information indicating an observation position, and set, from the plurality of pieces of attribute information for the area corresponding to the same position, a reliability of the area, based on the acquired position information.
 3. The apparatus according to claim 1, wherein the plurality of pieces of observation information are each a captured image obtained through imaging, by an imager, an area surrounding the moving body.
 4. The apparatus according to claim 1, wherein the plurality of pieces of observation information are information indicating a position of an object existing in the surroundings of the moving body.
 5. The apparatus according to claim 1, wherein the processing circuitry is further configured to set the reliability to a higher value as the proportion of the one or more past pieces of attribute information having the same value as the piece of attribute information is higher, and set the reliability to a lower value as the proportion of the one or more past pieces of attribute information having a same value as the piece of attribute information is lower.
 6. The apparatus according to claim 1, wherein the processing circuitry is further configured to calculate a travelable probability of the moving body based on the plurality of pieces of attribute information.
 7. The apparatus according to claim 6, wherein the processing circuitry is further configured to calculate the travelable probability of a first area where an attribute is a roadway to be a higher value than the travelable probability of a second area where the attribute is other than the roadway.
 8. The apparatus according to claim 6, wherein the processing circuitry is further configured to acquire, for each of a plurality of sensors that observes the surroundings of the moving body, probability information indicating a travelable probability of the surroundings of the moving body, and reliability information indicating the reliability of each of the plurality of pieces of attribute information of the surroundings of the moving body, and determine a final probability of the travelable probability of the surroundings of the moving body based on the acquired probability information and the acquired reliability information corresponding to each of the plurality of sensors.
 9. The apparatus according to claim 8, wherein the processing circuitry is further configured to determine the travelable probability of the surroundings of the moving body corresponding to a particular sensor having a highest reliability as the final probability.
 10. The apparatus according to claim 8, wherein the processing circuitry is further configured to perform a weighted summing of the travelable probabilities of each of the plurality of sensors to determine the final probability, wherein weights used in the weighted summing are determined according to the reliability information of each of the plurality of sensors.
 11. The apparatus according to claim 8, wherein the memory is configured to store therein the final probability.
 12. The apparatus according to claim 8, wherein the processing circuitry is further configured to calculate control information of the moving body based on the determined final probability.
 13. The apparatus according to claim 6, wherein the processing circuitry is further configured to calculate control information of the moving body.
 14. The apparatus according to claim 1, further comprising: a sensor configured to measure the plurality of pieces of observation information.
 15. A vehicle mounting the information processing apparatus according to claim
 1. 16. The information processing apparatus of claim 1, wherein the processing circuitry is further configured to calculate the reliability of the piece of attribute information corresponding to the given area as 1−|L(t)−L′(t)|, wherein L(t) is the numerical value of the piece of attribute information at time t for the given area, and L′(t) is an average value of the one or more past pieces of attribute information over a past N times for the given area, where N is an integer greater than
 2. 17. An information processing apparatus, comprising: a display configured to display information regarding an existence probability of an object existing in surroundings of a moving body, the existence probability being calculated based on each of a plurality of pieces of attribute information of the surroundings of the moving body and a reliability of each of the plurality of pieces of attribute information based on attribute information included in one or more past pieces of attribute information corresponding to an area at a same position as each respective piece of attribute information, wherein the information processing apparatus sets the reliability of a piece of attribute information corresponding to a given area based on a difference between (1) a proportion of the one or more past pieces of attribute information having a same value as the piece of attribute information, and (2) a value of the piece of attribute information corresponding to the given area, and at least one of the one or more past pieces of attribute information used to set the reliability of the piece of attribute information corresponding to the given area corresponds to a different position of the moving body than a position of the moving body corresponding to the piece of attribute information for the given area.
 18. The apparatus according to claim 17, wherein the reliability of a piece of attribute information is calculated using a plurality of past pieces of attribute information for a same area.
 19. The apparatus according to claim 17, wherein the existence probability of the object is an obstacle map.
 20. An information processing method, comprising: acquiring a plurality of pieces of observation information of surroundings of a moving body; generating a plurality of pieces of attribute information of the surroundings of the moving body based on the plurality of pieces of observation information; and setting a reliability of each of the plurality of pieces of attribute information of the surroundings of the moving body based on attribute information included in one or more past pieces of attribute information corresponding to an area at a same position as each respective piece of attribute information, wherein the setting comprises setting the reliability of a piece of attribute information corresponding to a given area based on a difference between (1) a proportion of the one or more past pieces of attribute information having a same value as the piece of attribute information, and (2) a value of the piece of attribute information corresponding to the given area, and at least one of the one or more past pieces of attribute information used to set the reliability of the piece of attribute information for information corresponding to the given area corresponds to a different position of the moving body than a position of the moving body corresponding to the piece of attribute information for the given area. 